We compare three methods in the context of transfer learning. As a baseline method, we consider the performance of a DNN trained on the target task without any extra knowledge. Additionally, we use the common transfer learning approach of pre-training a DNN on the source task and fine-tuning on the target task (PT/FT).
The underlying assumption of PT/FT is that the target model can leverage
prior knowledge present in the source task. This approach has been effective in many
applications, including ASR [1]} and
natural language processing [2]}.
